Computer databases now serve as storehouses for diverse types of information in a variety of forms of content including documents, images, audio files, videos, and practically any other type of content capable of being transferred to a digital format. The interconnected nature of today's computing environment offers the capability for users to have nearly instant access to this information regardless of their physical location.
Search interfaces serve as gateways to the vast information stored in these databases, but due to the tremendous amount and diverse types of digital data that is now accessible, searching for a broad category of data or mere keyword searching of these data stores can return an unmanageable number of results. The particular data being sought by the searcher can be obscured by a cumbersomely large result set, limiting the usefulness and efficiently of the search.
To assist the searcher in retrieving sought after data, a search interface can offer refinement options, such as suggested search queries based on the searcher's original input. Even though methods such as clustering similar search queries and matching those clusters may increase the coverage of search suggestion, they may fail to create specific suggestions for each query since the suggestions are the same for all queries in the cluster. Additionally, other methods that provide suggestions based on partial queries tend to sacrifice relevancy between original queries and their suggested queries.